The image representations extracted from the fully connected layers of deep networks pre-trained on ImageNet [1]} capture the overall shape of the object contained in the region of interest. The features extracted from the deeper layers encode class specific properties (i.e., shape, texture and color) and give superior classification performance as compared to features from shallower layers [2]}. Hence, we propose to extract the features from the output of the last convolutional block of ResNet-50 (Figure REF ). The output of the Conv5 block is a \(7\times 7\times 2048\)  dimensional array and is used as input of the FC-1000 layer. This large array is however, first converted to a 2048-dimensional vector by using a max-pool layer. We extract this 2048-dimensional vector and name it DRF. We do not use the FC-1000 layer for feature extraction because it is used as an output layer to classify the 1000 classes of the ImageNet dataset, which was used to pre-train this network. Our feature extraction method is different from the conventional method employed in previous deep networks such as VGGnet. The presence of multiple fully connected layers in the VGGnet makes the feature extraction straightforward. The only fully connected layer in ResNet is class specific to the ImageNet dataset. Therefore, we proposed to use the output of the last convolution block for DRF extraction.
